In comparison to PCA, LDA focuses on maximizing the separability between different ___________ rather than the variance of the data.
- classes
- features
- principal components
- variables
Unlike PCA, which focuses on the variance of the data, LDA emphasizes maximizing the separability between "different classes."
Describe a situation where a high Accuracy might be misleading, and a different metric (e.g., Precision, Recall, or F1-Score) might be more appropriate.
- When the dataset has equal classes, Precision is more appropriate
- When the dataset has only one class, Recall is more appropriate
- When the dataset is imbalanced, other metrics like Precision or Recall may be more informative
- nan
In imbalanced datasets, where one class significantly outnumbers the other, Accuracy can be misleading. Even a naive model predicting the majority class will have high Accuracy. Metrics like Precision, Recall, or F1-Score provide more insight into the model's performance on the minority class.
You are having difficulty interpreting the coefficients of your Logistic Regression model. How might the Logit function and Odds Ratio help in understanding them?
- By transforming coefficients into R-squared values
- By transforming coefficients into log-odds and allowing interpretation in terms of odds
- By transforming coefficients into odds
- By transforming coefficients into probabilities
The Logit function and Odds Ratio can help in understanding the coefficients by transforming them into log-odds and allowing interpretation in terms of the change in odds for a one-unit change in the predictor.
You are asked to apply Hierarchical Clustering to a dataset with mixed types of data (categorical and numerical). What challenges could arise and how would you tackle them?
- All of the above
- Computationally intensive clustering
- Difficulty in defining a suitable distance metric
- Inaccurate clustering due to the scale of numerical features
The primary challenge in clustering mixed types of data is defining a suitable distance metric that can handle both categorical and numerical features. You may need to standardize numerical features and find appropriate ways to measure distances for categorical attributes (e.g., using Gower distance). This choice will significantly influence the quality and interpretability of the clustering.
How is the amount of variance explained calculated in PCA?
- By dividing each eigenvalue by the sum of all eigenvalues
- By multiplying the eigenvalues with the mean
- By summing all eigenvalues
- By taking the square root of the eigenvalues
The amount of variance explained by each principal component in PCA is calculated by dividing the corresponding eigenvalue by the sum of all eigenvalues, and often expressed as a percentage.
You're working with a dataset that has clusters of various shapes and densities. Which clustering algorithm would be best suited for this, and why?
- DBSCAN
- Hierarchical Clustering
- K-Means
- Mean Shift
DBSCAN is best suited for clusters of various shapes and densities, as it's a density-based clustering method and doesn't rely on spherical assumptions about the data.
How do hyperplanes differ in hard-margin SVM and soft-margin SVM?
- Color difference
- Difference in dimensionality
- Difference in size
- Flexibility in handling misclassifications
Hard-margin SVM does not allow any misclassifications, while soft-margin SVM provides flexibility in handling misclassifications.
The F1-Score is the harmonic mean of _________ and _________.
- Accuracy, Recall
- Precision, Recall
- Precision, Specificity
- nan
The F1-Score is the harmonic mean of Precision and Recall. It gives equal weight to both these metrics, providing a balance between the ability to correctly identify positive cases and avoid false positives.
In a Multiple Linear Regression model, you discovered a significant interaction effect between two variables. How would you interpret this finding, and what implications might it have for the model?
- Add more variables
- Ignore the interaction
- No change to the model
- The effect of one variable depends on the level of the other
A significant interaction effect indicates that the effect of one variable on the response depends on the level of another variable. This means that the relationship between variables is not simply additive, and it may require the inclusion of an interaction term in the model to capture this complex relationship accurately.
What is regression in the context of Machine Learning?
- A method for classification
- A method for clustering
- A method for predicting a continuous output
- A method for text analysis
Regression in Machine Learning is a supervised learning technique used to predict a continuous output or numerical value based on input variables.